June 2011
Beginner to intermediate
744 pages
25h 11m
English
Content preview from Data Mining: Concepts and Techniques, 3rd Edition
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9.1 Bayesian Belief Networks
Chapter 8 introduced Bayes' theorem and naïve Bayesian classification. In this chapter, we describe Bayesian belief networks —probabilistic graphical models, which unlike naïve Bayesian classifiers allow the representation of dependencies among subsets of attributes. Bayesian belief networks can be used for classification. Section 9.1.1 introduces the basic concepts of Bayesian belief networks. In Section 9.1.2, you will learn how to train such models.
9.1.1 Concepts and Mechanisms
The naïve Bayesian classifier makes the assumption of class conditional independence, that is, given the class label of a tuple, the values of the attributes are assumed to be conditionally independent of one another. This simplifies computation. ...
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